Nowadays, with the rapid development of electronic information technologies, fields like electronic commerce, online banking, and public security have proposed new requirements on reliability and manners of identity authentication. Biological features are inherent attributes of a person, and have a powerful stability and individual difference. Therefore, performing identity authentication by using the biological features has attracted more and more attention. Performing identity authentication by using face features is the most natural and the most direct way. Therefore, it has an important application value to carry a deep research into the face recognition technology. Feature extraction is a core issue of the face recognition technology, and is directly related to a final accuracy of face recognition.
In the prior art, in a face image to be recognized, several neighborhood pixels are selected around each pixel; a gray value of a center pixel is used as a reference; adjacent pixels with gray values less than a gray value of an intermediate pixel are quantized to 0, and adjacent pixels with gray values greater than or equal to the gray value of the intermediate pixel are quantized to 1. Then, quantized values of the neighborhood pixels are connected in serial according to a specific direction to obtain a binary number; the binary number is further converted into a decimal number; and the decimal number is assigned to the center pixel. The foregoing operations are performed on all pixels in the image in sequence to obtain a local binary pattern (Local Binary Pattern, LBP for short) histogram of the image to be recognized. A feature vector of the LBP histogram of the image to be recognized is compared with a feature vector of an LBP histogram of a pre-registered face image. Then, the face recognition is completed.
However, this face recognition method has a low recognition accuracy.